In general, connecting related data and maintaining an accounting of related data connections are referred to as “connected component labeling” herein referred to as “CCL”. CCL is typically used for image analysis for computer vision. For example, an algorithm can be applied to an image, such as a binary image to separate object pixels from background pixels. Another use of CCL is to provide numeric labels for components identified in an image, such as a two-dimensional (2D) array. CCL is also used during the segmentation of other types of 1D, 2D, and 3D data such as financial data and digital audio. CCL and segmentation extract the needed information from data and images so that digital communications such as computer networks are not clogged with unnecessary high-bandwidth data.
Known methods to determine CCL include scanning an image to assign a provisional label and later determine a final label for each pixel. Scanning can assign such labels by locating neighbors and determining an appropriate label. Known methods include applying multi-pass labeling, two-pass labeling, depending on the complexity required for an application.
A problem with CCL methods is that memory requirements for many applications do not permit the required use of space for known CCL techniques. For example, the multi-pass labeling method requires repeated scanning and saving data in memory prior to determining a final label value for a single pixel in an image. What is needed is a CCL system and method that does not require the memory space of earlier known techniques and demands less bandwidth on networks.